In 2024, about 943.5 million people lived in urban regions in China and 464.8 million in rural. That year, the country had a total population of approximately 1.41 billion people. As of 2024, China was the second most populous country in the world. Urbanization in China Urbanization refers to the process by which people move from rural to urban areas and how a society adapts to the population shift. It is usually seen as a driving force in economic growth, accompanied by industrialization, modernization and the spread of education. Urbanization levels tend to be higher in industrial countries, whereas the degree of urbanization in developing countries remains relatively low. According to World Bank, a mere 19.4 percent of the Chinese population had been living in urban areas in 1980. Since then, China’s urban population has skyrocketed. By 2024, about 67 percent of the Chinese population lived in urban areas. Regional urbanization rates In the last decades, urbanization has progressed greatly in every region of China. Even in most of the more remote Chinese provinces, the urbanization rate surpassed 50 percent in recent years. However, the most urbanized areas are still to be found in the coastal eastern and southern regions of China. The population of Shanghai, the largest city in China and the world’s seventh largest city ranged at around 24 million people in 2023. China’s urban areas are characterized by a developing middle class. Per capita disposable income of Chinese urban households has more than doubled between 2010 and 2020. The emerging middle class is expected to become a significant driver for the continuing growth of the Chinese economy.

In 2024, approximately 67 percent of the total population in China lived in cities. The urbanization rate has increased steadily in China over the last decades. Degree of urbanization in China Urbanization is generally defined as a process of people migrating from rural to urban areas, during which towns and cities are formed and increase in size. Even though urbanization is not exclusively a modern phenomenon, industrialization and modernization did accelerate its progress. As shown in the statistic at hand, the degree of urbanization of China, the world's second-largest economy, rose from 36 percent in 2000 to around 51 percent in 2011. That year, the urban population surpassed the number of rural residents for the first time in the country's history.The urbanization rate varies greatly in different parts of China. While urbanization is lesser advanced in western or central China, in most coastal regions in eastern China more than two-thirds of the population lives already in cities. Among the ten largest Chinese cities in 2021, six were located in coastal regions in East and South China. Urbanization in international comparison Brazil and Russia, two other BRIC countries, display a much higher degree of urbanization than China. On the other hand, in India, the country with the worlds’ largest population, a mere 36.3 percent of the population lived in urban regions as of 2023. Similar to other parts of the world, the progress of urbanization in China is closely linked to modernization. From 2000 to 2024, the contribution of agriculture to the gross domestic product in China shrank from 14.7 percent to 6.8 percent. Even more evident was the decrease of workforce in agriculture.

China is a vast and diverse country and population density in different regions varies greatly. In 2023, the estimated population density of the administrative area of Shanghai municipality reached about 3,922 inhabitants per square kilometer, whereas statistically only around three people were living on one square kilometer in Tibet. Population distribution in China China's population is unevenly distributed across the country: while most people are living in the southeastern half of the country, the northwestern half – which includes the provinces and autonomous regions of Tibet, Xinjiang, Qinghai, Gansu, and Inner Mongolia – is only sparsely populated. Even the inhabitants of a single province might be unequally distributed within its borders. This is significantly influenced by the geography of each region, and is especially the case in the Guangdong, Fujian, or Sichuan provinces due to their mountain ranges. The Chinese provinces with the largest absolute population size are Guangdong in the south, Shandong in the east and Henan in Central China. Urbanization and city population Urbanization is one of the main factors which have been reshaping China over the last four decades. However, when comparing the size of cities and urban population density, one has to bear in mind that data often refers to the administrative area of cities or urban units, which might be much larger than the contiguous built-up area of that city. The administrative area of Beijing municipality, for example, includes large rural districts, where only around 200 inhabitants are living per square kilometer on average, while roughly 20,000 residents per square kilometer are living in the two central city districts. This is the main reason for the huge difference in population density between the four Chinese municipalities Beijing, Tianjin, Shanghai, and Chongqing shown in many population statistics.

Among countries with the highest number of overseas Chinese on each continent, the largest Chinese diaspora community is living in Indonesia, numbering more than ten million people. Most of these people are descendants from migrants born in China, who have moved to Indonesia a long time ago. On the contrary, a large part of overseas Chinese living in Canada and Australia have arrived in these countries only during the last two decades. China as an emigration country Many Chinese people have emigrated from their home country in search of better living conditions and educational chances. The increasing number of Chinese emigrants has benefited from loosened migration policies. On the one hand, the attitude of the Chinese government towards emigration has changed significantly. Overseas Chinese are considered to be strong supporters for the overall strength of Chinese culture and international influence. On the other hand, migration policies in the United States and Canada are changing with time, expanding migration opportunities for non-European immigrants. As a result, China has become one of the world’s largest emigration countries as well as the country with the highest outflows of high net worth individuals. However, the mass emigration is causing a severe loss of homegrown talents and assets. The problem of talent and wealth outflow has raised pressing questions to the Chinese government, and a solution to this issue is yet to be determined. Popular destinations among Chinese emigrants Over the last decades, English speaking developed countries have been popular destinations for Chinese emigrants. In 2022 alone, the number of people from China naturalized as U.S. citizens had amounted to over 27,000 people, while nearly 68,000 had obtained legal permanent resident status as “green card” recipients. Among other popular immigration destinations for Chinese riches are Canada, Australia, Europe, and Singapore.

China % of Population with Access to Water: City data was reported at 99.433 % in 2023. This records an increase from the previous number of 99.387 % for 2022. China % of Population with Access to Water: City data is updated yearly, averaging 96.120 % from Dec 1985 (Median) to 2023, with 31 observations. The data reached an all-time high of 99.433 % in 2023 and a record low of 63.900 % in 2000. China % of Population with Access to Water: City data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Percentage of Population with Access to Water.

In 2023, the urbanization rate in different provinces of China varied between 89.5 percent in Shanghai municipality and 38.9 percent in Tibet. The national average urbanization rate reached around 66.2 percent in 2023. Urbanization and economic development During China’s rapid economic development, the share of people living in cities increased from only 19.4 percent in 1980 to nearly 64 percent in 2020. Urbanization rates are now coming closer to those in developed countries. However, the degree of urbanization still varies significantly between different regions in China. This correlates generally with the level of economic development across different regions in China. In eastern Chinese regions with high personal income levels and high per capita GDP, more inhabitants are living in cities than in the countryside. Influence of geography Another reason for different urbanization rates lies in the huge geographic differences of regions in China. Basically, those regions with a low population density often also display lower urbanization rates, because their inhabitants live more scattered across the land area. These differences will most probably remain despite further economic progress.

The computed population density data for the map is based on a media CD released by ESRI in 2006. According to the media CD, China in 2006 comprised of 33 provinces. These include Tibet (now named Xizang, an autonomously administered region), Hong Kong and Macau (both of which are designated as special districts) along with Xingiang in the west, parts of which are involved in an unsettled border dispute with a neighboring country, as can be seen by a dotted line in google base map of the region and Taiwan. Compare this map with the population density map of 2002 that now has only 32 provinces...

Population in largest city in China was reported at 29867918 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

In 2021, around **** million people were estimated to be living in the urban area of Shanghai. Shanghai was the largest city in China in 2021, followed by Beijing, with around **** million inhabitants. The rise of the new first-tier cities The past decades have seen widespread and rapid urbanization and demographic transition in China. While the four first-tier megacities, namely Beijing, Shanghai, Guangzhou, and Shenzhen, are still highly attractive to people and companies due to their strong ability to synergize the competitive economic and social resources, some lower-tier cities are already facing declining populations, especially those in the northeastern region. Below the original four first-tier cities, 15 quickly developing cities are sharing the cake of the moving population with improving business vitality and GDP growth potential. These new first-tier cities are either municipalities directly under the central government, such as Chongqing and Tianjin, or regional central cities and provincial capitals, like Chengdu and Wuhan, or open coastal cities in the economically developed eastern regions. From urbanization to metropolitanization As more and more Chinese people migrate to large cities for better opportunities and quality of life, the ongoing urbanization has further evolved into metropolitanization. Among those metropolitans, Shenzhen's population exceeded **** million in 2020, a nearly ** percent increase from a decade ago, compared to eight percent in the already densely populated Shanghai. However, with people rushing into the big-four cities, the cost of housing, and other living standards, are soaring. As of 2020, the average sales price for residential real estate in Shenzhen exceeded ****** yuan per square meter. As a result, the fast-growing and more cost-effective new first-tier cities would be more appealing in the coming years. Furthermore, Shanghai and Beijing have set plans to control the size of their population to ** and ** million, respectively, before 2035.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

China

Analysis unit

Household Individual

Universe

National Population, Both sexes,18 and more years

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 1000

The sample is a representative national sample of China containing 40 county/city sample units to collect individual level data of, from a political cultural perspective, the values and attitudes currently held by Chinese citizens. With considerations of representativeness, feasibility, and budgetary constrains, it was decided this project would draw a subsidiary probability sample out of a master sample that RCCC created based on its previous national survey on environmental awareness of the general public in China conducted in 1998. The Environmental Awareness Survey, which was used as a master sample, was a national survey conducted through out the entire country. The target population was the same as the one defined for this survey. Through the stratification, the proportionally allocated multi-stage PPS (probability proportional to size) technique was employed in order to obtain the self-weighted household samples. There were different stages in the sampling procedure: Counties and county-level cities are taken as primary sampling units (PSUs). Family households are the basic sampling unit. Demographic data at all levels was obtained from The Demographic Data for Chinese Cities and Counties, 1997, published by the State Bureau of Statistics.

Nation wide, there were 2,860 county-level units for the first stage sampling (including 1,689 counties, 436 county-level cities, and 735 urban district--with administrative rank equivalent to county--in large cities). The total households were 337,659,447. This was the base for establishing the sampling frames. Some readjustments: Taking into account of cost and accessibility, only the provincial capitals (Lhasa and Urumchi) and their surrounding areas in Tibet and Sinkiang were included in the sampling frame; in other remote western provinces, a few areas that are extremely hard to access were left out as well. After such readjustment the sampling frame then includes 2,708 county-level units, of which the total households are 322,002,173. Compared to the target population, there was a 5.3% reduction (152 units) in the first stage sampling units. However, since the population density in the remote areas of the western provinces is very low, the reduction counts merely 1.4% of the total households in the sampling frame. Geographical administrative divisions of China were regarded as the primary labels of stratification, that is, each province was treated as an independent stratum. Allocation of target sampling units among the sampling stages was designed as following: 135 PSUs out of the first sampling (county-level) units; 2 secondary sampling (townshiplevel) units in each of the PSUs; then 2 third sampling (village-level) units in each of the SSUs; 25 households in each of the third sampling units, on average. Based on the proportional stratification principle, sample allocation to strata was proportional to the size of each stratum, by an equal probability of f = .0042%. Within each stratum (province), sample sizes were calculated and allocated proportionally to each of the sampling stages. A self-weighted national sample thus was obtained.

Multi-stage PPS: -The first stage: equidistance PPS was employed to draw the county sample. -The second stage: in each of the chosen county-level units, a sampling frame was created based on the data of townships/ward and size measurement; then the equidistance PPS is employed to choose the township/streets sample. -The third stage: a third sampling frame was obtained from each of the chosen township-level units (neighbourhoods, villages and size measurement), and, again, the equidistance PPS is employed to choose the village/neighbourhood sample. -The fourth stage: in each of the chosen village/neighbourhood units, the official list of households registration was obtained; using the size measurement of this unit and the desired number of households to count the sampling distance, then households were selected according to the sampling interval. Since the household registration also listed all family members of each of the household, respondents were drawn randomly immediately after the household drawing. The WVS-China sample was drawn out of the above described master sample.

Some readjustments: Primarily because of the budgetary constrains of the WVS project, six remote provinces in the master sample were excluded. They were: Hainan, Tibet, Gansu, Qinghai, Ningxia, and Sinkiang. These provinces are all with very low population density, and all together they count 5.1% of the total population and 4.6% of total households of the country. After the adjustments, seven of the 139 county-level units of the master sample were removed. Therefore, the target 40 PSUs were to be drawn out of the remaining 132 units.

Sampling Stages: -The first stage: 40 units were drawn from 132 county-level units of the master sample were removed. Therefore, the 40 PSUs were to be drawn out of the remaining 132 units. -The second stage: one unit was chosen randomly out of the 2 original township-level units (SSUs) in each of the 40 selected PSUs. -The third stage: one unit was chosen randomly out of the 2 original village-level units in each of the selected SSUs. -The fourth stage: from each of the chosen village-level units, 35 households were drawn out of the household registration list with equidistance, along with one respondent in each selected household.

Remarks about sampling: -Sample unit from office sampling: Housing

Mode of data collection

Face-to-face [f2f]

Research instrument

As a participating country-team of the World Values Survey (WVS), the Research Center of Contemporary China (RCCC) at Peking University implemented the WVS-China survey in 2001. The target population covers those who are between 18 and 65 of age (born between July 2, 1935 and July 1, 1982), formally registered and actually reside in dowelings within the households in China when the survey is conducted.

Response rate

The sample size was determined to be approximately 1,000 -- eligible individuals are to be drawn out of the above defined target population in China. Based on previous experience of response rate, it was decided to increase the target sample to 1,400 in order to reach a satisfied response rate. The final results are summarized as follows: - Target sample size: 1,400 - Sample drawn in the field: 1,385 - Completed, valid interviews: 1,000 - Response rate: 72.2% Summary of Non-Responses Types of Non-Responses (missing cases) % - Be away/not seen for several times: 145-37.7% - Be away for long time/be on a business trip/go abroad/travel:138-35.8% - The interviewer didnt write the reason: 23-6.0% - Rejection: 19-4.9% - Move/investigation reveals no this person: 15-3.9% - Impediments in body or language/at variance with qualification: 12-3.1% - Useless: 11-2.9% - Address is nor clear/cant find the address: 10-2.6% - A vacant house: 6-1.6% - Tenant: 6-1.6% - Total: 385-100%

Sampling error estimates

Estimated Error: 3,2

The data set of this article is related to the paper "Dynamical structure of social map in ancient China" (2022, Physica A, https://doi.org/10.1016/j.physa.2022.128209) . This article demonstrates the data of social relations between cities in ancient China, ranging from 618 AD to 1644 AD. The raw data of social associations between elites used to build social maps are extracted from the China Biographical Database. The raw data contain 14610 elites and 29673 social associations, which cover 366 cities in China. The dataset of this article is relevant both for social and natural scientists interested in the social and economic history of ancient China. The data can be used for further insights/analyses on the evolutionary pattern of geo-social architecture, and the geo-history from the viewpoint of social network.

The dataset contains $3$ files: "Networks.xlsx", "Coordinates.xlsx", and "SocialMap.html". The "Networks.xlsx" has 3 columns, representing the source node (city), target node (city), and weight of a link between two nodes, respectively. The "Networks.xlsx" contains $9$ sheets, which are the data for different dynasties named by Early Tang, Late Tang, Early Northern-Song, Late Northern-Song, Early Southern-Song, Late Southern-Song, Yuan, Early Ming, and Late Ming. Noticeably, the "Networks.xlsx" can be visualized by the network software of Gephi directly. The "Coordinates.xlsx" has 4 columns storing longitude and latitude for all cities that appeared in 9 networks. The first and second columns are English names and Chinese names of cities; the third and fourth columns are longitudes and latitudes of cities. The "SocialMap.html" provides a visualization platform, in which users could select and illustrate the evolution of social maps intuitively.

MCGD_Data_V2.2 contains all the data that we have collected on locations in modern China, plus a number of locations outside of China that we encounter frequently in historical sources on China. All further updates will appear under the name "MCGD_Data" with a time stamp (e.g., MCGD_Data2023-06-21)

You can also have access to this dataset and all the datasets that the ENP-China makes available on GitLab: https://gitlab.com/enpchina/IndexesEnp

Altogether there are 464,970 entries. The data include the name of locations and their variants in Chinese, pinyin, and any recorded transliteration; the name of the province in Chinese and in pinyin; Province ID; the latitude and longitude; the Name ID and Location ID, and NameID_Legacy. The Name IDs all start with H followed by seven digits. This is the internal ID system of MCGD (the NameID_Legacy column records the Name IDs in their original format depending on the source). Locations IDs that start with "DH" are data points extracted from China Historical GIS (Harvard University); those that start with "D" are locations extracted from the data points in Geonames; those that have only digits (8 digits) are data points we have added from various map sources.

One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates.

From June 2021 onward, we have adopted a different file naming system to keep track of versions. From MCGD_Data_V1 we have moved to MCGD_Data_V2. In June 2022, we introduced time stamps, which result in the following naming convention: MCGD_Data_YYYY.MM.DD.

UPDATES

MCGD_Data2025_02_27 inclues an update on locations extracted from Minguo zhengfu ge yuanhui keyuan yishang zhiyuanlu 國民政府各院部會科員以上職員錄 (Directory of staff members and above in the ministries and committees of the National Government). Nanjing: Guomin zhengfu wenguanchu yinzhuju 國民政府文官處印鑄局國民政府文官處印鑄局, 1944). We also made corrections in the Prov_Py and Prov_Zh columns as there were some misalignments between the pinyin name and the name in Chines characters. The fine now includes 465,128 entries.

MCGD_Data2024_03_23 includes an update on locations in Taiwan from the Asia Directories. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown").

MCGD_Data2023.12.22 contains all the data that we have collected on locations in China, whatever the period. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). The dataset also includes locations outside of China for the purpose of matching such locations to the place names extracted from historical sources. For example, one may need to locate individuals born outside of China. Rather than maintaining two separate files, we made the decision to incorporate all the place names found in historical sources in the gazetteer. Such place names can easily be removed by selecting all the entries where the 'Province' data is missing.

If you want to use this data, please cite our article:Xiong, S., Zhang, X., Lei, Y., Tan, G., Wang, H., & Du, S. (2024). Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain. Remote Sensing of Environment, 312, 114344.The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

As amalgams of the different spatial logics of other places, reassembled by globalization and the fantasies of real estate development, cities today are becoming what Michel Foucault has termed heterotopias. Assemblies of ruins, theme parks, entirely copied towns, simulacra, business districts on a globalized template, reconstructed historic districts, settlements, and ghost towns are finding a new expression in the contemporary world. Nowhere has this been more visible recently than in China, and areas are coming under China’s developmental influence. Copied cities, ghost cities, and large-scale Chinese investments in Africa are heterotopias because they contain the idea of accumulating different times, cultures, and countries within one place, just as a theme park has all these different place experiences in a bounded zone outside of its own time and culture. Ella Raidel has explored these phenomena through film and cinematic virtual reality, and this artist’s book reviews and reflects on the last two decades of her award-winning work. In Ella Raidel’s films, urbanism and architecture, theory, politics, social change, and image production are intertextually presented, opening a discursive space for investigation and commentary. This book will be interesting for art and film practitioners and architecture, film, urbanization, and infrastructure students, especially those who see cinema as a way of exploring these subjects.

This file provides bilingual Chinese-English transcripts of nine focus group discussions (FGDs) carried out in three Chinese cities in June and July 2012. The focus groups were commissioned by the authors from the Research Center for Contemporary China (RCCC) at Peking University as part of the ESRC project ‘Performance evaluations, trust and utilization of health care in China: understanding relationships between attitudes and health-related behaviour’. Local residents over the age of 30 took part in the discussions, which were moderated by a senior researcher from RCCC. The FGDs dealt with five main issues: how people know about changes in the health care system changes; how people make decisions to see a doctor when they are unwell; health care system evaluations; trust in doctors and the health care system; and what kind of a system people would like. The FGDs use a series of fictional scenarios (vignettes) to elicit responses concerning what influences people’s decisions about going to a doctor when they are unwell.This interdisciplinary project establishes a new collaboration among UK researchers and a leading Chinese social research team, to conduct the first major study of Chinese people's attitudes towards their health care. The project's core theoretical contribution is to understanding the relationships between attitudes and health-related behaviours, focussing particularly on how people evaluate their health system, their trust in doctors and the health system, and their utilization of preventive and curative health services. Previous quantitative research on health in China has examined the influence on utilization of age and gender, incomes, insurance protection, distance to health service providers and perceived health care needs. Yet work done in other countries has shown that attitudes, including performance evaluations and trust, can impact on people's decisions about when and where to use health services. At the same time, qualitative studies in China have suggested that people are often critical of performance and that there is a crisis of trust in doctors and the health care system. Our project is the first systematic study of these attitudes and how they influence utilization. The three cities chosen for focus group discussions, Chifeng, Yueyang and Shaoxing, represented respectively a city below the national average, close to the average and above the average in terms of GDP per capita. Two stratifications were used to select participants (see Focus Group Participant Profiles for details): Stratification One: of the general population by location and individual circumstances. This stratification was used in Chifeng and Shaoxing; all participants were local residents. In Chifeng, two discussions was conducted in the city itself and one discussion in a rural area under the city’s jurisdiction. In Shaoxing, one discussion was conducted in the city itself and one in a rural village within the city’s jurisdiction. Stratification Two: of patients by individual circumstances. This stratification was used in Yueyang. The participants in each of the four focus groups were screened by asking whether they had had contact with the health care system during the last two weeks in connection with an injury or illness; and what type of medical insurance they possessed. The initial intention was to stratify patients according to whether they reported suffering acute or chronic conditions. However, the difficulty of recruiting participants prevented this. The stratification of patients was thus according to their type of insurance. Nearly all participants on the first day of discussions (#4 and #5) had medical insurance equivalent to Urban Employees Basic Medical Insurance, whilst participants on the second day of discussions (#6 and #7) did not have this level of insurance. Most of these were members of the Rural Cooperative Medical Scheme, which gives them only limited entitlements to reimbursement of medical expenses in Yueyang.

This data collection includes 'life story' interviews with Russian-speaking women from Russia, Ukraine, and Belarus who have married Chinese citizens and moved for their married lives to the People's Republic of China. Most of the recorded interviews were transcribed verbatim in Russian. Some of the non-recorded conversations are summarised in English. The topics covered in the interviews include the women's journeys to China, their experiences of family, social, and working lives, the challenges of legal, socio-cultural and emotional adaptation, and the questions of citizenship and immigration status for women and their children.The growth of mega-cities and more generally rapid urbanization in China not only include hundreds of millions internal migrants, but an increasing number of foreign (including Taiwanese and returning ethnic Chinese) migrants as well. At present, foreign migrants fill relatively small and specific skills and knowledge gaps, but also include marriage migrants, traders, investors, retirees and unskilled workers. However as China's population growth levels off, population ageing sets in. China's working age population is set to decline, slowly at first but increasingly rapidly, especially roughly after 2025. Moreover, the population's sex imbalance will become ever more pronounced and China will face an increasing shortage of marriageable and working age people. Although international migration is set to make an important contribution to these increasing demographic and labour market shortages in China, little research has as yet been done. Our project will provide estimates and projections of the role of international and internal migration on population dynamics in China. The central focus of our project is on the impact of the second demographic transition in China, including family changes, ageing, migration and regional population changes. We will collect vital data on the interaction between labour markets and population dynamics, the consequences of migration, integration policies in China, EU-China mobility, and shifting patterns of inequality and the cultural division of labour. The project therefore speaks directly to the issues under the theme Understanding Population Change of the Europe - China call for collaborative research. This research data collection includes the transcripts of life story interviews with Russian-speaking women from the Soviet Union who have married a Chinese national and moved for a family life to the People's Republic of China. The research participants for this project were recruited through a snowballing method. A written call for participation and project information were distributed through established contacts and social media, inviting interested parties to contact the researcher. A consent form with the project information was shared with prospective participants prior to the interview. The interviews took place face-to-face or through a video or audio function in Skype or in Wechat, China's most popular social media platform.

This statistic shows the population density in urban areas of China in 2023, by region. In 2023, cities in Heilongjiang province had the highest population density in China with around ***** people living on one square kilometer on average. However, as the administrative areas of many Chinese cities reach beyond their contiguous built-up urban areas - and this by varying degree, the statistical significance of the given figures may be limited. By comparison, the Chinese province with the highest overall population density is Jiangsu province in Eastern China reaching about 7956 people per square kilometer in 2023.

The Yangtze River Delta urban agglomeration (YRDUA) is China’s most representative region with remarkable economic development vitality. The purpose of this study is to provide valuable data analysis to actively respond to the population aging in China. We mainly focus on the spatial and temporal evolution of population aging in YRDUA from 2000 to 2020 using city-level population data. This study constructs a multi-dimensional index system to measure population aging including population aging degree, speed, and density. It finds out: (1) the elderly population rate (EPR), the elder-child ratio (ECR), and the elderly dependency ratio (EDR) in the YRDUA area are gradually increasing from 2000 to 2020. In addition, the trends of these indicators in various cities and regions are relatively consistent. All 27 cities in YRDUA entered an aging society, from the primary to the moderate aging stage from 2000 to 2010 and from the moderate to the hyper aging stage from 2010 to 2020. (2) the absolute and relative growth rate of EPR is increasing from 2000 to 2020. However, the absolute and relative growth rate of ECR is increasing from 2000 to 2010 and then decreasing from 2010 to 2020. These results indicate that the two-child policy adopted by the Chinese government plays a positive role. (3) the density level of the elderly population in the YRDUA evolved from low in 2000 to middle in 2010 and then to high in 2020. (4) There are remarkable differences in the process of population aging among three provinces and one city. The contribution of this study is mainly reflected in two aspects: firstly, it constructs a multi-dimensional index system to measure population aging; secondly, using this multi-dimensional index system, it systematically observes the spatial and temporal evolution of population aging from 2000 to 2020 in the Yangtze River Delta Urban Agglomeration.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in China. Power Plant emissions from all power plants in China were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, metro area, lat/lon, and plant id for each individual power plant. Only Power Plants that had a listed longitude and latitude in CARMA's database were mapped. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/47

It is an important basis for the research on the prevention and early warning mechanism of alien invasive plants in China to figure out the types of alien plants in China, where they come from, how to enter China, what kind of groups of these alien plants are, as well as their biological and ecological characteristics. The information of alien plants recorded in Flora of China (Chinese edition), Flora of China (English edition) and their records in the Chinese province flora is very limited since various reason. At present, there is no complete database reflecting the information of alien plants in China. By integrating materials related to alien plants in recent years, and textual research on the origin and added habits of alien plants through literature, and then using computer network, databases and big data analysis technical means, after information treatment and taxonomic correction, with reconstruction of the classification, this paper finally determines the species directory data set of the book. There are 14710 data in this set, with 14710 groups of Chinese alien plants belonging to 3233 genera and 283 families (including 13401 original species, 332 hybrids, 2 chimeras, 458 subspecies, 503 varieties and 14 forms). Each taxa includes basic information such as categories of plants, Chinese family, family name, Chinese genus, genus, Chinese name, alias, scientific name, author, survival status, survival time, growth status, country or region of origin and province of Chinese distribution. The data set shows that alien plants have accounted for a considerable proportion in the composition of the Chinese plant species (at present, there are 37464 groups of native plants in China (including infraspecies), and with 14710 alien groups, the proportion of exotic plants is as high as 28.19%). In terms of survival status, cultivated plants account for 91% of all exotic plants, escape plants account for 7.36%, naturalized plant account for 6.69% and invasive plants account for 2.66%; The analysis of life forms shows that perennial groups account for the vast majority of alien plants (13625 species, about 92.6%), and the number of herbs (8937 species, about 60.8%) is more than that of trees (2752 species, about 18.7%), shrubs (4916 species, about 33.4%) as well as other life forms. Most of the alien plants in China were from North America (4242 species), Africa (3707 species), South America (3645 species), Asia (3102 species), Europe (1690 species) and Oceania (1305 species). The top 10 provinces and cities in China with more exotic plants are Taiwan (6122 species), Beijing (5244 species), Fujian (3667 species), Guangdong (3544 species), Yunnan (3404 species), Shanghai (2924 species), Jiangsu (2183 species), Jiangxi (1789 species), Zhejiang (1658 species) and Hubei (973 species). This data set is the first comprehensive and systematic collation of alien plants in China. It can be used as a reference for research related to alien plants, as well as basic data for plant diversity research. It can also be used as a reference book for people in agriculture, forestry, grassland, gardens, herbal medicine, nature protection and environmental protection, as well as teachers and students in colleges and universities.